WO2023178970A1 - Medical data processing method, apparatus and device, and storage medium - Google Patents

Abstract

The present application relates to the field of artificial intelligence, and discloses a medical data processing method, apparatus and device, and a storage medium, used for improving the accuracy of doctor-patient matching. The medical data processing method comprises: determining, according to medical data, whether a user conforms to a user type; if the user conforms to the user type, matching a disease template corresponding to the user according to the medical data; performing information filling on the disease template on the basis of a disease diagnosis path to obtain an end node and a user answer path, and generating a user demand according to the end node and the user answer path; if the user demand is doctor inquiry, inputting the medical data and the user answer path into a medical data processing model for disease data processing to obtain a predicted node; performing comparative analysis on the predicted node and the end node; and if the comparison result indicates that the predicted node and the end node are the same, performing doctor matching on the user to obtain a target doctor. In addition, the present application also relates to blockchain technology, and the user demand can be stored in a blockchain node.

Description

Medical data processing methods, devices, equipment and storage media

This application claims priority to the Chinese patent application filed with the China Patent Office on March 24, 2022, with application number 202210296654.8 and the invention name "Medical Data Processing Methods, Devices, Equipment and Storage Media", the entire content of which is incorporated by reference. In application.

Technical field

This application relates to the field of artificial intelligence, and in particular, to a medical data processing method, device, equipment and storage medium.

Background technique

With the development of the medical Internet and informatization, the problem of imbalance of medical resources has been alleviated. Services such as online appointment registration, remote doctor consultation, prescription recommendation and opening, and drug purchase have provided patients with convenience and helped digest some of the diseases. The heavy business of offline hospitals. At the same time, a large amount of doctor information, patient information, and conversation data have been accumulated in Internet platforms or information systems, forming valuable Internet medical big data, especially the historical activity records of each patient (such as consultation, registration, drug purchase, etc. ), which will play a great role in the patient’s future disease diagnosis and identification of medical needs.

The inventor realized that the common practice of online consultation is to first match patients with departments and doctors through their chief complaints and basic information, and usually use some classification models to classify patients. At present, the satisfaction level of matching patients and doctors needs to be improved, mainly Because there is little user information, it is difficult to accurately match hospital doctor data that is large in volume, comes from many sources, and has different data structures. That is, the accuracy of existing solutions is low.

Contents of the invention

This application provides a medical data processing method, device, equipment and storage medium to improve the accuracy of doctor-patient matching.

In order to achieve the above purpose, the first aspect of this application provides a medical data processing method, which includes: obtaining medical data corresponding to the user to be processed, and judging whether the user meets the preset user type based on the medical data, wherein , the medical data includes user gender, user age and symptom data; if the user meets the preset user type, a disease template corresponding to the user is matched according to the medical data, wherein the disease template includes user The disease diagnosis path to be filled in; fill in the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate a user based on the end node and the user answer path demand; if the user demand is a doctor's consultation, input the medical data and the user answer path into the preset medical data processing model for disease data processing to obtain a prediction node; compare the prediction node and the end The nodes perform comparison analysis to obtain the comparison result; if the comparison result is that the prediction node and the end node are the same, then perform doctor matching on the user based on the prediction node and the end node to obtain the target doctor.

A second aspect of the present application provides a medical data processing device, including: a memory and at least one processor, with instructions stored in the memory; and the at least one processor calls the instructions in the memory, so that the The medical data processing equipment performs the following steps: obtains the medical data corresponding to the user to be processed, and determines whether the user meets the preset user type based on the medical data, wherein the medical data includes user gender, user age and Symptom data; if the user meets the preset user type, match the disease template corresponding to the user according to the medical data, wherein the disease template includes a disease diagnosis path to be filled in by the user; based on the disease diagnosis The path fills the disease template with information, obtains the end node and user answer path corresponding to the disease template, and generates user demand based on the end node and the user answer path; if the user demand is a doctor's consultation, Then the medical data and the user answer path are input into the preset medical data processing model for disease data processing to obtain a prediction node; a comparison analysis is performed on the prediction node and the end node to obtain a comparison result; if If the comparison result is that the prediction node and the end node are the same, then doctor matching is performed on the user based on the prediction node and the end node to obtain a target doctor.

A third aspect of the present application provides a computer-readable storage medium. Instructions are stored on the computer-readable storage medium. When the instructions are executed by a processor, the following steps are implemented: Obtain the medical data corresponding to the user to be processed. , and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data; if the user meets the preset user type, then based on the The medical data matches a disease template corresponding to the user, where the disease template includes a disease diagnosis path to be filled in by the user; information is filled in the disease template based on the disease diagnosis path to obtain the end corresponding to the disease template nodes and user answer paths, and generate user needs based on the end node and the user answer path; if the user needs are doctor consultations, input the medical data and the user answer path into preset medical data The processing model performs disease data processing to obtain prediction nodes; performs comparison analysis on the prediction node and the end node to obtain a comparison result; if the comparison result is that the prediction node and the end node are the same, then Perform doctor matching on the user according to the prediction node and the end node to obtain a target doctor.

A fourth aspect of the present application provides a medical data processing device, wherein the medical data processing device includes: an acquisition module for acquiring medical data corresponding to a user to be processed, and determining whether the user is a user based on the medical data. Comply with the preset user type, wherein the medical data includes user gender, user age and symptom data; a matching module configured to match the user with the user according to the medical data if the user complies with the preset user type. The corresponding disease template, wherein the disease template includes a disease diagnosis path to be filled in by the user; a filling module, used to fill the disease template with information based on the disease diagnosis path to obtain the end node corresponding to the disease template and The user answers the path, and generates user requirements according to the end node and the user answer path; a processing module, used to input and preset the medical data and the user answer path if the user requirement is a doctor's consultation. The medical data processing model performs disease data processing to obtain prediction nodes; an analysis module is used to compare and analyze the prediction node and the end node to obtain a comparison result; a generation module is used to compare the comparison result If the prediction node and the end node are the same, then perform doctor matching on the user based on the prediction node and the end node to obtain the target doctor.

In the technical solution provided by this application, it is judged based on medical data whether the user meets the preset user type. The medical data includes user gender, user age and symptom data; if the user meets the preset user type, the medical data is matched with The disease template corresponding to the user, where the disease template includes the disease diagnosis path to be filled in by the user; the disease template is filled with information based on the disease diagnosis path to obtain the end node and user answer path corresponding to the disease template, and based on the end node and user answer path Generate user needs; if the user needs are doctor consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; perform a comparison and analysis on the prediction node and the end node to obtain the comparison If the comparison result is that the prediction node and the end node are the same, the user will be matched with doctors based on the prediction node and the end node to obtain the target doctor. This application obtains preliminary diagnosis results and other medical information by performing self-diagnosis on the user. This more comprehensive information can be used to more accurately match doctors and patients, avoiding some invalid communication or wrong matching. User self-diagnosis and medical need identification can be carried out based on the disease diagnosis path template, which improves the efficiency of the online consultation process.

Description of the drawings

Figure 1 is a schematic diagram of an embodiment of the medical data processing method in the embodiment of the present application;

Figure 2 is a schematic diagram of another embodiment of the medical data processing method in the embodiment of the present application;

Figure 3 is a schematic diagram of an embodiment of the medical data processing device in the embodiment of the present application;

Figure 4 is a schematic diagram of another embodiment of the medical data processing device in the embodiment of the present application;

Figure 5 is a schematic diagram of an embodiment of the medical data processing equipment in the embodiment of the present application.

Detailed ways

This application provides a medical data processing method, device, equipment and storage medium to improve the accuracy of doctor-patient matching.

In order to enable those skilled in the art to better understand the solutions of the present application, the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

The terms "first", "second", "third", "fourth", etc. (if present) in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects without necessarily using Used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments described herein can be practiced in sequences other than those illustrated or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., processes, methods, systems, products, or devices that comprise a series of steps or units and are not necessarily limited to those expressly listed. steps or units, but may include other steps or units not expressly listed or inherent to such processes, methods, products or apparatuses.

For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1. One embodiment of the medical data processing method in the embodiment of the present application includes:

101. Obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data. The medical data includes user gender, user age and symptom data;

It should be noted that the medical data input by the user to be processed includes personal gender, age and symptom data. User types include those who do not know the disease they suffer from, have unclear consultation needs, or actively choose smart disease diagnosis. The server identifies the user type based on the medical data input by the user, and determines whether the user to be processed meets the above user types. It should be noted that in order to ensure the authenticity of the symptom data, the symptom data can be obtained from medical websites, medical institution databases, etc. Symptom data is text data that includes disease words and symptom words. Symptom data can be obtained from medical websites or medical institution data through preset crawlers. The medical website or medical institution data records the user's disease words and symptom words to generate symptom data. , or the user can directly input disease words and symptom words to obtain symptom data.

It can be understood that the execution subject of this application can be a medical data processing device, or a terminal or a server, which is not specifically limited here. The embodiments of this application are explained by taking the server as the execution subject as an example. The embodiments of this application can obtain and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. . Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning/deep learning. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and content delivery network (Content Delivery Network, CDN), as well as cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

102. If the user meets the preset user type, match the disease template corresponding to the user based on the medical data, where the disease template includes the disease diagnosis path to be filled in by the user;

Specifically, if the user meets the preset user type, the server enters the self-diagnosis module. The server first matches the disease template based on the previous information. The disease template contains the disease diagnosis path, similar to a tree diagram, including multiple question options and different answers. May lead to different branches. By the user answering the questions in the template, the system will fill in the template nodes and throw new questions until it reaches the diagnosis node or the end node. It should be noted that the disease template is obtained through computer-assisted medical literature mining and doctor editing and review.

103. Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and user answer path;

It should be noted that when the user fills in the questions in the template, the server will fill in the template nodes and throw new questions until all diagnostic nodes or end nodes are filled. Based on the final node and the intermediate user answer path, the server predicts a user's needs through a deep learning network model, including doctor consultation, finding a hospital, buying medicine, physical examination, registration, chronic disease management, comprehensive consultation, etc. The server will also predict a user's needs based on the user's needs. Recommend relevant functions and guide users to the next service module.

104. If the user's demand is for a doctor's consultation, input the medical data and user answer path into the preset medical data processing model to process the disease data and obtain the prediction node;

Specifically, if the user needs to consult a doctor, the server will input the medical data and user answer path into a preset medical data processing model for disease data processing. The preset medical data processing model includes an input layer and a multi-layer convolution. Neural network and normalization layer, among which, the multi-layer convolutional neural network is used to perform convolution operation on the input vector, and the normalization layer is used to perform logistic regression operation on the feature vector, and finally outputs the prediction node.

105. Perform comparison analysis on the prediction node and the end node, and obtain the comparison results;

Specifically, the server performs comparison analysis on the prediction node and the end node to obtain the comparison result. The server calculates the similarity between the prediction node and the end node. When the similarity exceeds the preset threshold, the server determines the prediction node and the end node. The end nodes are the same. When the similarity does not exceed the preset threshold, the server determines that the predicted node is different from the end node.

106. If the comparison result is that the prediction node and the end node are the same, the user will be matched with doctors based on the prediction node and the end node to obtain the target doctor.

Specifically, if the comparison result is that the prediction node and the end node are the same, the server calculates the credibility of the prediction node and the end node to obtain the target credibility. The server sorts the target credibility and selects the target credibility. The degree corresponds to the maximum value of the candidate doctor, and the maximum value of the candidate doctor is obtained. The server uses the candidate doctor corresponding to the maximum value as the target doctor.

Further, the server stores the user requirements in the blockchain database, and the details are not limited here.

In the embodiment of this application, it is judged based on the medical data whether the user meets the preset user type, where the medical data includes the user's gender, user age and symptom data; if the user meets the preset user type, the corresponding user is matched based on the medical data. disease template, where the disease template includes a disease diagnosis path to be filled in by the user; fill in the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate a user based on the end node and user answer path Demand; if the user's demand is a doctor's consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; perform a comparison and analysis on the prediction node and the end node to obtain the comparison result. ; If the comparison result is that the prediction node and the end node are the same, the user is matched with doctors based on the prediction node and the end node to obtain the target doctor. This application obtains preliminary diagnosis results and other medical information by self-diagnosing the user, so that it can be used This part of more comprehensive information allows for more accurate doctor-patient matching, avoiding some ineffective communication or mismatching. User self-diagnosis and medical need identification are performed based on the disease diagnosis path template, which improves the efficiency of the online consultation process.

Please refer to Figure 2. Another embodiment of the medical data processing method in the embodiment of this application includes:

201. Obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data;

Specifically, the server receives the medical data input by the user to be processed, where the medical data includes user gender, user age and symptom data; the server generates a user portrait corresponding to the user based on the user's gender, user age and symptom data; the server calculates whether the user portrait Match the similarity corresponding to the preset user type, and compare the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, the server determines that the user matches the preset user type. Specifically, if the similarity is less than the preset target value, the server determines that the user does not meet the preset user type. The server receives the medical data input by the user to be processed. The user can input medical data through the preset terminal. The medical data includes The user's personal information and symptom data; the server generates a user portrait corresponding to the user based on the user's gender, user age, and symptom data, and the server generates a triplet corresponding to the user, which includes gender, age, and symptoms; the server calculates whether the user portrait Comply with the similarity corresponding to the preset user type, and compare the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, the server determines that the user matches the preset user type. If the similarity is below If the preset target value is set, the server determines that the user does not meet the above three categories of users.

202. If the user meets the preset user type, match the disease template corresponding to the user based on the medical data, where the disease template includes the disease diagnosis path to be filled in by the user;

Specifically, if the user meets the preset user type, the server will extract keywords from the medical data to obtain the target keywords; the server will match the target keywords with multiple preset disease templates to obtain the corresponding information for each disease template. keyword hit rate; the server sorts the keyword hit rate corresponding to each disease template, and uses the disease template with the highest keyword hit rate as the disease template corresponding to the user, where the disease template includes the disease diagnosis path to be filled in by the user. . Specifically, if the user meets the preset user type, the server performs keyword extraction on the medical data to obtain the target keywords. The server performs keyword extraction by first converting the medical data into text data, and then using the preset OCR text recognition The model extracts text data to obtain standard text data. Finally, the target keywords are obtained by removing duplicate content from the standard text data. The server matches the target keywords with multiple preset disease templates to obtain each disease template. Corresponding keyword hit rate, the keyword hit rate is the number of keyword hits; the server sorts the keyword hit rate corresponding to each disease template, and uses the disease template with the highest keyword hit rate as the disease corresponding to the user Template, wherein the disease template includes a disease diagnosis path to be filled in by the user.

203. Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and user answer path;

Specifically, the server obtains the run-through rate and recommended click-through rate based on the disease diagnosis path; the server fills the disease template with information based on the run-through rate and recommended click-through rate to obtain the filled disease template; the server extracts data from the filled disease template , obtain the end node and user answer path corresponding to the disease template; the server generates user requirements based on the end node and user answer path, where user requirements include doctor consultation and offline consultation. Specifically, the server obtains the pass rate and recommended click rate based on the disease diagnosis path. Among them, the server optimizes the template through indicators such as the pass rate and recommended click rate obtained from the statistical analysis of online business data. The pass rate refers to whether the user can go At the final node of the template, the server fills the disease template with information based on the run-through rate and recommended click rate to obtain the filled disease template; the server extracts data from the filled disease template and obtains the end node and user answer corresponding to the disease template. Path; the server generates user requirements based on the end node and user answer path, where user requirements include doctor consultation and offline consultation. Among them, a template can be used as a node of other templates, that is, a sub-template. There may be inclusion relationships between templates. For example, a general ophthalmology template may include several eye disease templates as sub-templates, and the weights of template nodes will be adjusted based on the user's historical medical records such as consultation information.

204. If the user's demand is for a doctor's consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing to obtain prediction nodes;

Specifically, if the user needs to consult a doctor, the server will input the medical data and user answer path into the preset medical data processing model; the server will use the convolutional neural network in the medical data processing model to convolve the medical data and user answer path. Product operation is performed to obtain the feature vector corresponding to the medical data; the server performs feature normalization processing on the feature vector to obtain the prediction node. It should be noted that the preset medical data processing model includes an input layer, a convolutional neural network and an output layer. The input layer: one-hot vector coding layer (one-hot vector); the hidden layer: the convolution operation function, which is linear unit; output layer: the dimensions are the same as those of the input layer, and logistic regression is used. The server performs one-hot vector encoding on the target information through the input layer to obtain a low-dimensional vector. For example, the low-dimensional vector is [0,0,0,1,0,1,0,0]. The server performs one-hot vector encoding through the convolutional neural network layer. The low-dimensional vector performs feature abstraction operations to obtain abstract feature values, which are also feature vectors; the server performs logistic regression operations on the abstract feature values through the output layer to obtain prediction nodes, where the logistic regression operation is a softmax regression operation, where, The prediction node can be the result of the first online diagnosis, such as: online first diagnosis of blepharitis, conjunctivitis, respiratory infection, etc.

205. Perform comparison analysis on the prediction node and the end node, and obtain the comparison results;

Specifically, the server performs comparison analysis on the prediction node and the end node to obtain the comparison result. The server calculates the similarity between the prediction node and the end node. When the similarity exceeds the preset threshold, the server determines the prediction node and the end node. The end nodes are the same. When the similarity does not exceed the preset threshold, the server determines that the predicted node is different from the end node.

206. If the comparison result is that the prediction node and the end node are the same, then the user is matched with a doctor based on the prediction node and the end node to obtain the target doctor;

Specifically, if the comparison result is that the prediction node and the end node are the same, the server obtains multiple candidate doctors in the preset candidate doctor set; the server calculates the credibility of the multiple candidate doctors based on the prediction node and the end node, and obtains each The target credibility corresponding to each candidate doctor; the server ranks multiple candidate doctors in the candidate doctor set according to the target credibility corresponding to each candidate doctor, and uses the highest-ranked candidate doctor as the doctor matching the user, and obtains Target Doctor. Specifically, the server sorts the preset candidate doctor list based on the target credibility to obtain the ranking. The server is based on the user and considers the user preference and candidate doctor workload. The server defines the user's preference degree to measure the user's preference for candidate doctors. Index, the server obtains the preference index corresponding to the user through the weighted average method of doctor-patient matching index. After the server calculates the matching degree of all candidate doctors to the user, it recommends the target doctor based on the highest target credibility and preference index. The server matches users with highly relevant target doctors to improve the user's consultation experience. In addition, some target doctors with specific expertise can also be matched with relevant users, thus avoiding the waste of medical resources.

207. If the comparison result is that the prediction node and the end node are not the same, delete the prediction node and generate auxiliary information corresponding to the user based on the disease template;

Specifically, if the comparison result is that the prediction node and the end node are the same, the server calculates the credibility of the prediction node and the end node to obtain the target credibility. The server sorts the target credibility and selects the target credibility. The degree corresponds to the maximum value of the candidate doctor, and the maximum value of the candidate doctor is obtained. The server uses the candidate doctor corresponding to the maximum value as the target doctor.

208. Match the user with doctors based on the auxiliary information and the end node, and obtain the target doctor corresponding to the user.

Specifically, if the server results do not match, the matching doctor model will remove the diagnosis result information and use the department, chief complaint and some node information to match the doctor. The template questions answered by the user will also be transmitted to the doctor as auxiliary information. The server matches the user with doctors based on the auxiliary information and the end node, and obtains the target doctor corresponding to the user.

Further, the server stores the user requirements in the blockchain database, and the details are not limited here.

In the embodiment of this application, it is judged based on the medical data whether the user meets the preset user type, where the medical data includes the user's gender, user age and symptom data; if the user meets the preset user type, the corresponding user is matched based on the medical data. disease template, where the disease template includes a disease diagnosis path to be filled in by the user; fill in the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate a user based on the end node and user answer path Demand; if the user's demand is a doctor's consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; perform a comparison and analysis on the prediction node and the end node to obtain the comparison result. ; If the comparison result is that the prediction node and the end node are the same, the user is matched with doctors based on the prediction node and the end node to obtain the target doctor. This application obtains preliminary diagnosis results and other medical information by self-diagnosing the user, so that it can be used This part of more comprehensive information allows for more accurate doctor-patient matching, avoiding some ineffective communication or mismatching. User self-diagnosis and medical need identification are performed based on the disease diagnosis path template, which improves the efficiency of the online consultation process.

The medical data processing method in the embodiment of the present application is described above, and the medical data processing device in the embodiment of the present application is described below. Please refer to Figure 3. An example of the medical data processing device in the embodiment of the present application includes:

The acquisition module 301 is used to obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data;

The matching module 302 is configured to match a disease template corresponding to the user according to the medical data if the user meets the preset user type, where the disease template includes a disease diagnosis path to be filled in by the user;

The filling module 303 is used to fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path. ;

The processing module 304 is used to input the medical data and the user answer path into a preset medical data processing model to perform disease data processing if the user demand is a doctor's consultation to obtain a prediction node;

The analysis module 305 is used to compare and analyze the prediction node and the end node to obtain a comparison result;

The generation module 306 is configured to perform doctor matching on the user based on the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same.

Further, the server stores the user requirements in the blockchain database, and the details are not limited here.

In the embodiment of this application, it is judged based on the medical data whether the user meets the preset user type, where the medical data includes the user's gender, user age and symptom data; if the user meets the preset user type, the corresponding user is matched based on the medical data. disease template, where the disease template includes a disease diagnosis path to be filled in by the user; fill in the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate a user based on the end node and user answer path Demand; if the user's demand is a doctor's consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; perform a comparison and analysis on the prediction node and the end node to obtain the comparison result. ; If the comparison result is that the prediction node and the end node are the same, the user is matched with doctors based on the prediction node and the end node to obtain the target doctor. This application obtains preliminary diagnosis results and other medical information by self-diagnosing the user, so that it can be used This part of more comprehensive information allows for more accurate doctor-patient matching, avoiding some ineffective communication or mismatching. User self-diagnosis and medical need identification are performed based on the disease diagnosis path template, which improves the efficiency of the online consultation process.

Please refer to Figure 4. Another embodiment of the medical data processing device in the embodiment of the present application includes:

The acquisition module 301 is used to obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data;

The matching module 302 is configured to match a disease template corresponding to the user according to the medical data if the user meets the preset user type, where the disease template includes a disease diagnosis path to be filled in by the user;

The filling module 303 is used to fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path. ;

The processing module 304 is used to input the medical data and the user answer path into a preset medical data processing model to perform disease data processing if the user demand is a doctor's consultation to obtain prediction nodes;

The analysis module 305 is used to compare and analyze the prediction node and the end node to obtain a comparison result;

The generation module 306 is configured to perform doctor matching on the user based on the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same.

Optionally, the acquisition module 301 is specifically used for:

Receive medical data input by the user to be processed, wherein the medical data includes user gender, user age, and symptom data; generate a user portrait corresponding to the user based on the user gender, the user age, and the symptom data; Calculate whether the user portrait conforms to the similarity corresponding to the preset user type, and compare the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, determine the similarity The above user matches the preset user type.

Optionally, the matching module 302 is specifically used for:

If the user meets the preset user type, keyword extraction is performed on the medical data to obtain target keywords; the target keywords are matched with multiple preset disease templates to obtain each disease template. Corresponding keyword hit rate; sort the keyword hit rate corresponding to each disease template, and use the disease template with the highest keyword hit rate as the disease template corresponding to the user, where the disease template includes the user Disease diagnosis path to be filled in.

Optionally, the filling module 303 is specifically used for:

Obtain the run-through rate and the recommended click-through rate based on the disease diagnosis path; fill in the disease template with information based on the run-through rate and the recommended click-through rate to obtain the filled disease template; The template performs data extraction to obtain the end node and user answer path corresponding to the disease template; user requirements are generated based on the end node and the user answer path, where the user requirements include doctor consultation and offline consultation.

Optionally, the processing module 304 is specifically used to:

If the user demand is a doctor's consultation, the medical data and the user answer path are input into the preset medical data processing model; the medical data and the medical data are processed through the convolutional neural network in the medical data processing model. The user answer path is subjected to a convolution operation to obtain the feature vector corresponding to the medical data; the feature vector is subjected to feature normalization processing to obtain the prediction node.

Optionally, the generation module 306 is specifically used for:

If the comparison result is that the prediction node and the end node are the same, obtain multiple candidate doctors in the preset candidate doctor set; perform a search on the multiple candidate doctors based on the prediction node and the end node. Calculate the credibility to obtain the target credibility corresponding to each candidate doctor; rank the multiple candidate doctors in the candidate doctor set according to the target credibility corresponding to each candidate doctor, and assign the highest-ranked The candidate doctor is used as the doctor matching the user, and the target doctor is obtained.

Optionally, the medical data processing device also includes:

Deletion module 307, configured to delete the prediction node if the comparison result is that the prediction node and the end node are not the same, and generate auxiliary information corresponding to the user based on the disease template; based on The auxiliary information and the end node perform doctor matching on the user to obtain the target doctor corresponding to the user.

Further, the server stores the user requirements in the blockchain database, and the details are not limited here.

In the embodiment of this application, it is judged based on the medical data whether the user meets the preset user type, where the medical data includes the user's gender, user age and symptom data; if the user meets the preset user type, the corresponding user is matched based on the medical data. disease template, where the disease template includes a disease diagnosis path to be filled in by the user; fill in the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate a user based on the end node and user answer path Demand; if the user's demand is a doctor's consultation, input the medical data and user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; perform a comparison and analysis on the prediction node and the end node to obtain the comparison result. ; If the comparison result is that the prediction node and the end node are the same, the user is matched with doctors based on the prediction node and the end node to obtain the target doctor. This application obtains preliminary diagnosis results and other medical information by self-diagnosing the user, so that it can be used This part of more comprehensive information allows for more accurate doctor-patient matching, avoiding some ineffective communication or mismatching. User self-diagnosis and medical need identification are performed based on the disease diagnosis path template, which improves the efficiency of the online consultation process.

The above Figures 3 and 4 describe in detail the medical data processing device in the embodiment of the present application from the perspective of modular functional entities. The following describes the medical data processing device in the embodiment of the present application in detail from the perspective of hardware processing.

Figure 5 is a schematic structural diagram of a medical data processing device provided by an embodiment of the present application. The medical data processing device 500 may vary greatly due to different configurations or performance, and may include one or more processors (central processing units). , CPU) 510 (eg, one or more processors), one or more storage media 530 (eg, one or more mass storage devices) storing application programs 533 or data 532. The storage medium 530 may be short-term storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the medical data processing device 500 . Furthermore, the processor 510 may be configured to communicate with the storage medium 530 and execute a series of instruction operations in the storage medium 530 on the medical data processing device 500 .

The medical data processing device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 520, and/or, one or more operating systems 531, such as Windows Serve , Mac OS X, Unix, Linux, FreeBSD and more. Those skilled in the art can understand that the structure of the medical data processing equipment shown in Figure 5 does not constitute a limitation on the medical data processing equipment. It may include more or fewer components than shown in the figure, or some components may be combined, or different components may be used. Component placement.

This application also provides a medical data processing device. The medical data processing device includes a processor. When the computer readable instructions are executed by the processor, the processor performs the steps of the medical data processing method in the above embodiments.

This application also provides a computer-readable storage medium. The computer-readable storage medium can be a non-volatile computer-readable storage medium. The computer-readable storage medium can also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on the computer, they cause the computer to perform the following steps: obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset based on the medical data. user type, wherein the medical data includes user gender, user age and symptom data; if the user meets the preset user type, the disease template corresponding to the user is matched according to the medical data, wherein the The disease template includes a disease diagnosis path to be filled in by the user; the disease template is filled with information based on the disease diagnosis path to obtain the end node corresponding to the disease template and the user answer path, and based on the end node and the The user answer path generates user needs; if the user need is a doctor's consultation, the medical data and the user answer path are input into the preset medical data processing model for disease data processing to obtain prediction nodes; the prediction is The node and the end node are compared and analyzed to obtain a comparison result; if the comparison result is that the predicted node and the end node are the same, then the user is evaluated based on the predicted node and the end node. Doctor matching, get the target doctor.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function, etc.; the storage data area may store information based on the blockchain node. Use the created data, etc.

The blockchain referred to in this application is a new application model of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain is essentially a decentralized database. It is a series of data blocks generated using cryptographic methods. Each data block contains a batch of network transaction information and is used to verify its Validity of information (anti-counterfeiting) and generation of the next block. Blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program code. .

As mentioned above, the above embodiments are only used to illustrate the technical solution of the present application, but not to limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still make the foregoing technical solutions. The technical solutions described in each embodiment may be modified, or some of the technical features may be equivalently replaced; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions in each embodiment of the present application.

Claims (20)

A medical data processing method, wherein the medical data processing method includes: Obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data; If the user meets the preset user type, match the disease template corresponding to the user according to the medical data, wherein the disease template includes a disease diagnosis path to be filled in by the user; Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path; If the user demand is a doctor's consultation, the medical data and the user answer path are input into the preset medical data processing model for disease data processing to obtain prediction nodes; Perform comparison analysis on the prediction node and the end node to obtain a comparison result; If the comparison result is that the prediction node and the end node are the same, doctor matching is performed on the user based on the prediction node and the end node to obtain a target doctor. The medical data processing method according to claim 1, wherein said obtaining the medical data corresponding to the user to be processed, and judging whether the user meets the preset user type according to the medical data includes: Receive medical data input by the user to be processed, wherein the medical data includes user gender, user age and symptom data; Generate a user portrait corresponding to the user based on the user's gender, the user's age and the symptom data; Calculate whether the user portrait matches the similarity corresponding to the preset user type, and compare the similarity with the preset target value; If the similarity is greater than or equal to the preset target value, it is determined that the user meets the preset user type. The medical data processing method according to claim 1, wherein if the user meets a preset user type, matching the disease template corresponding to the user according to the medical data includes: If the user meets the preset user type, keyword extraction is performed on the medical data to obtain target keywords; Match the target keywords with multiple preset disease templates respectively to obtain the keyword hit rate corresponding to each disease template; The keyword hit rate corresponding to each disease template is sorted, and the disease template with the highest keyword hit rate is used as the disease template corresponding to the user, where the disease template includes a disease diagnosis path to be filled in by the user. The medical data processing method according to claim 1, wherein the disease template is filled with information based on the disease diagnosis path to obtain the end node and user answer path corresponding to the disease template, and the end node is obtained according to the end node. The nodes and user answer paths generate user requirements, including: Obtain the pass rate and recommendation click rate based on the disease diagnosis path; Fill the disease template with information based on the run-through rate and the recommended click-through rate to obtain a filled disease template; Extract data from the filled disease template to obtain the end node and user answer path corresponding to the disease template; User requirements are generated based on the end node and the user answer path, where the user requirements include doctor consultation and offline consultation. The medical data processing method according to claim 1, wherein if the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model for disease data processing. , get the predicted nodes, including: If the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model; Perform a convolution operation on the medical data and the user answer path through the convolutional neural network in the medical data processing model to obtain the feature vector corresponding to the medical data; Perform feature normalization processing on the feature vector to obtain prediction nodes. The medical data processing method according to claim 1, wherein if the comparison result is that the prediction node and the end node are the same, then the user is processed based on the prediction node and the end node. Doctor matching, get the target doctor, including: If the comparison result is that the prediction node and the end node are the same, obtain multiple candidate doctors in the preset candidate doctor set; Perform credibility calculations on the plurality of candidate doctors based on the prediction node and the end node to obtain the target credibility corresponding to each candidate doctor; The plurality of candidate doctors in the candidate doctor set are ranked according to the target credibility corresponding to each candidate doctor, and the highest-ranked candidate doctor is used as the doctor matching the user to obtain a target doctor. The medical data processing method according to any one of claims 1 to 6, wherein if the comparison result is that the prediction node and the end node are the same, then according to the prediction node and the end node After the end node performs doctor matching on the user and obtains the target doctor, the medical data processing method further includes: If the comparison result is that the prediction node and the end node are different, the prediction node is deleted, and auxiliary information corresponding to the user is generated based on the disease template; Perform doctor matching on the user based on the auxiliary information and the end node to obtain the target doctor corresponding to the user. A medical data processing device, wherein the medical data processing device includes: a memory and at least one processor, with instructions stored in the memory; and the at least one processor calls the instructions in the memory, so that The medical data processing equipment performs the following steps: Obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data; If the user meets the preset user type, match the disease template corresponding to the user according to the medical data, wherein the disease template includes a disease diagnosis path to be filled in by the user; Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path; If the user demand is a doctor's consultation, the medical data and the user answer path are input into the preset medical data processing model for disease data processing to obtain prediction nodes; Perform comparison analysis on the prediction node and the end node to obtain a comparison result; If the comparison result is that the prediction node and the end node are the same, doctor matching is performed on the user based on the prediction node and the end node to obtain a target doctor. The medical data processing device according to claim 8, wherein the obtaining the medical data corresponding to the user to be processed and judging whether the user meets the preset user type according to the medical data includes: Receive medical data input by the user to be processed, wherein the medical data includes user gender, user age and symptom data; Generate a user portrait corresponding to the user based on the user's gender, the user's age and the symptom data; Calculate whether the user portrait matches the similarity corresponding to the preset user type, and compare the similarity with the preset target value; If the similarity is greater than or equal to the preset target value, it is determined that the user meets the preset user type. The medical data processing device according to claim 8, wherein if the user meets a preset user type, matching the disease template corresponding to the user according to the medical data includes: If the user meets the preset user type, keyword extraction is performed on the medical data to obtain target keywords; Match the target keywords with multiple preset disease templates respectively to obtain the keyword hit rate corresponding to each disease template; The keyword hit rate corresponding to each disease template is sorted, and the disease template with the highest keyword hit rate is used as the disease template corresponding to the user, where the disease template includes a disease diagnosis path to be filled in by the user. The medical data processing device according to claim 8, wherein the disease template is filled with information based on the disease diagnosis path to obtain the end node and user answer path corresponding to the disease template, and the end node is obtained according to the end node. The nodes and user answer paths generate user requirements, including: Obtain the pass rate and recommendation click rate based on the disease diagnosis path; Fill the disease template with information based on the run-through rate and the recommended click-through rate to obtain a filled disease template; Extract data from the filled disease template to obtain the end node and user answer path corresponding to the disease template; User requirements are generated based on the end node and the user answer path, where the user requirements include doctor consultation and offline consultation. The medical data processing device according to claim 8, wherein if the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model for disease data processing. , get the predicted nodes, including: If the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model; Perform a convolution operation on the medical data and the user answer path through the convolutional neural network in the medical data processing model to obtain the feature vector corresponding to the medical data; Perform feature normalization processing on the feature vector to obtain prediction nodes. The medical data processing device according to claim 8, wherein if the comparison result is that the prediction node and the end node are the same, then the user is processed based on the prediction node and the end node. Doctor matching, get the target doctor, including: If the comparison result is that the prediction node and the end node are the same, obtain multiple candidate doctors in the preset candidate doctor set; Perform credibility calculations on the plurality of candidate doctors based on the prediction node and the end node to obtain the target credibility corresponding to each candidate doctor; The plurality of candidate doctors in the candidate doctor set are ranked according to the target credibility corresponding to each candidate doctor, and the highest-ranked candidate doctor is used as the doctor matching the user to obtain a target doctor. The medical data processing device according to any one of claims 8-13, wherein if the comparison result is that the prediction node and the end node are the same, then according to the prediction node and the end node After the end node performs doctor matching on the user and obtains the target doctor, the medical data processing device further includes: If the comparison result is that the prediction node and the end node are different, the prediction node is deleted, and auxiliary information corresponding to the user is generated based on the disease template; Perform doctor matching on the user based on the auxiliary information and the end node to obtain the target doctor corresponding to the user. A computer-readable storage medium. Instructions are stored on the computer-readable storage medium. When the instructions are executed by a processor, the following steps are implemented: Obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data; If the user meets the preset user type, the disease template corresponding to the user is matched according to the medical data, wherein the disease template includes the disease diagnosis path to be filled in by the user; Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path; If the user demand is a doctor's consultation, the medical data and the user answer path are input into the preset medical data processing model for disease data processing to obtain prediction nodes; Perform comparison analysis on the prediction node and the end node to obtain a comparison result; If the comparison result is that the prediction node and the end node are the same, doctor matching is performed on the user based on the prediction node and the end node to obtain a target doctor. The computer-readable storage medium according to claim 15, wherein the obtaining medical data corresponding to the user to be processed, and determining whether the user meets a preset user type based on the medical data includes: Receive medical data input by the user to be processed, wherein the medical data includes user gender, user age and symptom data; Generate a user portrait corresponding to the user based on the user's gender, the user's age and the symptom data; Calculate whether the user portrait matches the similarity corresponding to the preset user type, and compare the similarity with the preset target value; If the similarity is greater than or equal to the preset target value, it is determined that the user meets the preset user type. The computer-readable storage medium according to claim 15, wherein if the user meets a preset user type, matching a disease template corresponding to the user according to the medical data includes: If the user meets the preset user type, keyword extraction is performed on the medical data to obtain target keywords; Match the target keywords with multiple preset disease templates respectively to obtain the keyword hit rate corresponding to each disease template; The keyword hit rate corresponding to each disease template is sorted, and the disease template with the highest keyword hit rate is used as the disease template corresponding to the user, where the disease template includes a disease diagnosis path to be filled in by the user. The computer-readable storage medium according to claim 15, wherein the disease template is filled with information based on the disease diagnosis path to obtain the end node and user answer path corresponding to the disease template, and according to the The end node and the user answer path generate user requirements, including: Obtain the pass rate and recommendation click rate based on the disease diagnosis path; Fill the disease template with information based on the run-through rate and the recommended click-through rate to obtain a filled disease template; Extract data from the filled disease template to obtain the end node and user answer path corresponding to the disease template; User requirements are generated based on the end node and the user answer path, where the user requirements include doctor consultation and offline consultation. The computer-readable storage medium according to claim 15, wherein if the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model to perform disease data processing. Process and obtain prediction nodes, including: If the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model; Perform a convolution operation on the medical data and the user answer path through the convolutional neural network in the medical data processing model to obtain the feature vector corresponding to the medical data; Perform feature normalization processing on the feature vector to obtain prediction nodes. A medical data processing device, wherein the medical data processing device includes: The acquisition module is used to obtain the medical data corresponding to the user to be processed, and determine whether the user meets the preset user type based on the medical data, where the medical data includes user gender, user age and symptom data; A matching module, configured to match a disease template corresponding to the user according to the medical data if the user meets the preset user type, wherein the disease template includes a disease diagnosis path to be filled in by the user; A filling module, configured to fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements based on the end node and the user answer path; A processing module, configured to input the medical data and the user answer path into a preset medical data processing model to process disease data if the user demand is a doctor's consultation, and obtain prediction nodes; An analysis module, used to compare and analyze the prediction node and the end node to obtain a comparison result; A generation module configured to perform doctor matching on the user based on the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same.

Images